Key Takeaways & Executive Findings
- •• • Construction dust and road dust are the dominant local sources of PM2.5 at the Tongzhou Dongguan site, with contributions significantly exceeding other sources, underscoring the need for targeted dust control measures. • • Meteorological conditions, specifically low boundary layer height, weak winds, and high humidity, are closely correlated with abnormal PM2.5 increases, indicating that pollution episodes are exacerbated under stagnant atmospheric conditions. • • The integrated tracing method, combining qualitative fingerprint analysis and quantitative modeling, successfully identified local source contributions, providing a replicable framework for county-town scale air quality management. • • Backward trajectory analysis revealed that catering sources from the west and southwest directions significantly contributed to PM2.5 episodes, highlighting the importance of regional transport and local emission interactions.
Abstract
This study proposes an integrated source apportionment framework that synergistically integrates pollution source classification, atmospheric dispersion modeling, backward trajectory analysis, weighted trajectory clustering, and forward contribution estimation to accurately target peak reduction at localized air pollution hotspots. Applied at the County-Town Scale in Beijing, this method was employed to investigate pollution episodes at the Tongzhou Dongguan monitoring site. Source classification relied on a pollution fingerprint database and temporal concentration profiles, while local contributions were quantified through combined air quality modeling and monitoring data. Forward and backward trajectory analyses enabled the identification of potential source regions and key contributors. Results indicate that construction dust, road dust, and emissions from the catering industry were the dominant local sources, with construction and road dust contributing most prominently to PM2.5 concentrations. Furthermore, abnormal PM2.5 increases were closely linked to low boundary layer height, weak winds, and high humidity, emphasizing the role of meteorological conditions in pollution accumulation. The proposed framework proves effective in pinpointing local pollution sources and offers a scientific basis for targeted air quality management at finer spatial scales.
1. Introduction
Existing air quality management at the county-town scale faces a critical bottleneck: conventional source apportionment methods, such as emission inventory-based approaches and receptor models, often lack the spatial and temporal resolution needed to pinpoint localized pollution sources during peak episodes. These methods are frequently hampered by insufficient source inventory precision and inadequate representation of dispersion under complex meteorological conditions, leading to high uncertainty in local contribution estimates. As a result, environmental agencies struggle to implement targeted mitigation measures that can effectively reduce PM2.5 concentrations at specific hotspots.
This study addresses this gap by proposing a multi-faceted tracing framework that integrates pollution source classification, numerical source apportionment, backward trajectory analysis, weighted trajectory assessment, and forward contribution calculation. Applied at the county-town scale in Beijing, the method leverages a pollution fingerprint database and temporal concentration profiles for qualitative source identification, coupled with air quality modeling and monitoring data for quantitative local contribution assessment. This approach enables precise identification of source regions and contribution magnitudes, as demonstrated at the Tongzhou Dongguan site, thereby offering a practical solution for fine-scale air pollution control.
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FAN Shoubin, ZHAO Yuncheng, QU Song, ZHANG Chunjie, JIAO Yufang, NIE Ruixian, ZHANG Xuanyu, LONG Teng (2026). Research and Application of County-Town Scale Air Pollution Tracing Method. Environmental Chemistry. https://doi.org/10.7524/j.issn.0254-6108.2025032004
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Frequently Asked Questions
What are the dominant local sources of PM2.5 at the Tongzhou Dongguan site, and how were their contributions quantified?
Construction dust and road dust were identified as the dominant local sources, with contributions significantly higher than other sources. Their contributions were quantified using a combination of air quality modeling and monitoring data, integrated with forward and backward trajectory analyses.
How do meteorological conditions influence PM2.5 concentrations at the study site?
Abnormal PM2.5 increases were closely linked to low boundary layer height, weak winds, and high humidity. These conditions favor pollutant accumulation, leading to higher concentrations during pollution episodes.
What is the methodological advantage of the proposed tracing framework over traditional source apportionment methods?
The framework integrates multiple techniques—pollution source classification, numerical modeling, backward trajectory analysis, and forward contribution estimation—allowing for both qualitative and quantitative identification of local sources at a finer spatial scale, which is often lacking in traditional methods.
Can this method be applied to other regions or scales?
Yes, the method is designed to be scalable and adaptable. While applied at the county-town scale in Beijing, the framework can be extended to other regions with similar pollution challenges, provided that local emission inventories and monitoring data are available.
What are the practical implications for air quality management?
The findings emphasize the need for targeted dust control measures, such as intelligent monitoring systems and optimized suppression strategies, as well as enhanced road cleaning and traffic emission controls. The method provides a scientific basis for precise pollution reduction strategies at the local level.
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